Intermittent Energy Resources Research Articles

Coordination of modular nano grid energy management using multi-agent AI architecture

Many innovative creations of businesses and academics are a result of the ever-increasing intricacy of the contemporary, renewable electricity, and fuel networks. To handle the complicated aspects of networks, the right architectural and testing techniques, methodologies, theories, and accompanying technologies are required. Intermittent sustainable energy resources like wind and solar present some difficulties in managing energy production and use. This study provides a revolutionary multi-agent AI system-based smart controlling strategy to manage the power requirements of a building with sustainable power resources. To meet the demands of the workload, the suggested approach uses a hierarchical multiple-agent network based on collaboration and communication among intelligent entities. An intelligent grid network comprising of multiple intelligent nano grids connected to a sustainable energy resource with tariff monitoring and intelligent power flow monitoring been modeled for energy-sharing and electrical reliability enhancement. The network, which features imbalanced three-phase diodes bridging rectifier output, is combined using two distinct Renewable Energy Sources (RESs). These two RESs include a photovoltaic unit for the first nano grid and a wind power generation unit for the second nano grid. This method is primarily based on auto-similar networks, which are suitable and simple to apply for multiple agent networks.

Exergoeconomic assessment of a high-efficiency compressed air energy storage system

Energy storage systems have a critical part in enabling greater use of intermittent energy resources. For a sustainable energy supply mix, compressed air energy storage systems offer several advantages through the integration of practical and flexible types of equipment in the overall energy system. The primary advantage of these systems is the management of the duration of the peak load of multiple generation sources in ‘islanded operation’ without connection to the electricity network. Here, research introduces and analyzes a novel multigeneration system for electrical power, cooling, heating, potable water, sodium hypochlorite, and hydrogen production to improve the industrial aspects of the system for more profitability and efficiency with lower environmental impacts. The system involves three main sub-systems: CAES, combined cooling, heat & power, and a desalination unit. To assess the performance of the system, technical, economic, exergoeconomic, and exergoenvironmental studies are conducted. The results regarding the energy and exergy studies reveal that the system presents great potential for reliable operation during peak demand hours. The round-trip efficiency is 74.5 % producing 1721 kW of electrical power with concurrent cooling and heating loads at 272.9 and 334.6 kW, respectively. Economically, the levelized costs of heating/cooling, clean desalinated water, and electricity are 26.4 US$/GJ, 2 US$/lit, and 4.5 cents/kWh, respectively. Finally, the outcomes of the exergoenvironmental study display that the sustainability index for the system is 1.7.

An Adjustable Scenario Optimisation Approach in Operating PV-Rich Distribution Systems

The embrace of intermittent distributed energy resources (DER) on the demand side increases the concerns around the safe operation of distribution networks. Existing solutions either neglect the available information around the distribution of uncertain parameters or underestimate DER capability in providing real-time recourse actions. In this paper, we propose an adjustable scenario optimisation-based solution approach that maximises the usage of DER generation within the operation horizon, while providing the required level of network security. Our modelling i) guarantees hard constraints satisfaction, ii) allows fast-acting devices, e.g., inverters, to adjust in response to live realisations, and iii) enables the operators to maintain the soft constraints with a required probability using a joint chance-constrained (JCC) program. We reformulate the JCCs using the scenario optimisation approach that directly takes empirical data and provides a pre-specified bound on the risk of constraint violation. To increase the scalability of our proposal, we introduce an approximation of JCCs to be used within the scenario optimisation approach. Through extensive comparisons and simulations, we show that our approach outperforms the conventional scenario optimisation and adjustable robust approaches respectively by 83% and 80% in our experiments.

Hierarchical optimal allocation of BESS using APT-FPSO based on stochastic programming model considering voltage sensitivity and eigenvalues analyses

Integration of energy storage systems (ESSs) to microgrids (MGs) due to the growing penetration of intermittent energy resources is a great motivation to develop studies in the context of optimal sizing and placement of these systems. In this paper, a multi-objective bi-level optimization problem is addressed based on the stochastic nature and dynamic model of power system elements.As a preliminary step, the optimal size and capacity of the battery energy storage system (BESS) are determined to minimize frequency fluctuations. This level has been performed based on three approaches considering the daily intra-hour stochastic data. In the secondary level, the dynamic behaviour of all MG elements has been considered and correspondingly a dynamic objective function based on system eigenvalues and voltage sensitivity index (VSI) has been developed. According to the proposed objective function, the secondary step for sizing and placement of the battery units has been performed by adaptive particularly tuneable fuzzy particle swarm optimization (APT-FPSO) algorithm to reduce power losses, improve the voltage profile/ stability, and mitigate low-frequency fluctuations. Finally, the effectiveness of the proposed approach is evaluated using time-domain simulations, voltage sensitivity studies, and eigenvalue analysis.

CO2 electrolysis toward acetate: A review

Storing renewable electricity from intermittent energy resources by turning waste CO2 into versatile chemicals is promising for sustainable energy economy. In this process, multi-carbon products, especially acetate, are hard to achieve due to the high energy barrier and low selectivity for C–C coupling. Targeting acetate as a CO2 electroreduction reactions (CO2RR) end-product is perspective to reform the current acetate industry. Herein, we summarize the two major CO2-to-acetate routes: the direct route via CO2RR and the indirect one via CO reduction reaction (CORR). We firstly summarized reaction mechanisms for different routes; then discussed several categories of catalysts for acetate production via CO2RR or CORR. We also correlate the theoretical calculations and experimental results to better understand the catalyst design principles. Finally, challenges and opportunities in current established processes are proposed. We aim to build a systematic understanding of current CO2-to-acetate strategies and offer practical design guidelines for industrial CO2-to-acetate production.

Event-Trigger-Based Distributed Optimization Approach for Two-Level Optimal Model of Isolated Power System With Switching Topology

Due to the uncertain output of intermittent energy resources and dynamic communication topology, it brings a great challenge for the optimal security control of isolated power system. To address this problem, this article proposes a two-level optimal control strategy with event-triggered switching mechanisms. For ensuring the security of the isolated power system, event-triggered switching mechanisms are proposed in the upper-level model to decrease potential risk of supply security and voltage stability, which can ensure system security as well as a low switching cost. In the lower-level model, a distributed optimization with switching topology is developed to minimize power generation cost under the above switching mechanisms, and the convergence ability of the proposed distributed optimization method is well proved with a uniformly globally exponentially stable condition. The obtained simulation results reveal that the proposed optimization approach can properly deal with the security issue of an isolated power system as well as dynamically minimize the economic cost.

Online distribution system state estimation via stochastic gradient algorithm

Distribution network operation is becoming more challenging because of the growing integration of intermittent and volatile distributed energy resources (DERs). This motivates the development of new distribution system state estimation (DSSE) paradigms that can operate at fast timescale based on real-time data stream of asynchronous measurements enabled by modern information and communications technology. To solve the real-time DSSE with asynchronous measurements effectively and accurately, this paper formulates a weighted least squares DSSE problem and proposes an online stochastic gradient algorithm to solve it. The performance of the proposed scheme is analytically guaranteed and is numerically corroborated with realistic data on IEEE 123-bus feeder. • Power-injection-based distribution system state estimation. • Online state estimation with stochastic gradient algorithm. • Highly efficient and accurate algorithm. • Online simulation of 1-second granularity with real data.

Status, Challenges and Future Directions of Blockchain Technology in Power System: A State of Art Review

Intermittent distributed energy resources (DERs) add challenges to the modern power system network. On the other hand, information and communication technology (ICT) is changing traditional electricity grids into smart grids, which facilitates a decentralized system in which prosumers may participate in energy trading. Smart grids, DER integration, and network connectivity are adding complexity to the power system network day by day; Blockchain technology might be a great tool to manage the network’s operational complexity. The Blockchain provides for quicker, frictionless, secure, and transparent transactions. With the addition of smart contracts, it may be utilized to manage the expanding complexity of the contemporary power system. In this study, the authors focus on the scope, challenges, and potential future direction of Blockchain technology application in the power system. Blockchain has received interest and has been used for decentralized power system applications in recent years, but it is still young and has scalability, decentralization, and security concerns. This article discusses the interfaces and the possibilities that can assure trust, security, and transparency in decentralized power system applications and make a decentralized power system and power market possible.

Fuzzy Logic–Based Decentralized Voltage–Frequency Control and Inertia Control of a VSG-Based Isolated Microgrid System

This work proposes the use of fuzzy-logic-based voltage frequency control (VFC) and adaptive inertia to improve the frequency response of a virtual synchronous generator (VSG)-based isolated microgrid system. The joint VFC and inertial control scheme is proposed to limit frequency deviations in these isolated microgrid systems, mainly caused by the increasing penetration of intermittent distributed energy resources, which lack rotational inertia. The proposed controller uses artificial neural networks (ANN) to estimate the exponent of voltage-dependent loads and modulate the system frequency by adjusting the output voltage of the VSGs, which increases the system’s active power reserves while providing inertial control by adjusting the inertia of VSGs to minimize frequency and VSG DC-link voltage excursions. A genetic algorithm (GA)-based optimization strategy is developed to optimally adjust the parameters of the fuzzy logic controller to diminish the impact of disturbances on the system. In addition, the proposed technique is illustrated through simulations within the framework of a test system based on the CIGRE medium-voltage benchmark under various circumstances. The results of these simulations demonstrate that the proposed control strategy outperforms existing methods.

NiFe2O4 @Co3O4 heterostructure with abundant oxygen vacancies as a bifunctional electrocatalyst for overall water splitting

Exploring robust and high-performance bifunctional electrocatalysts for overall water splitting is crucial for converting electric power to hydrogen energy, especially for the flexible utilization of intermittent green energy resources. Herein, pine-needle-like NiFe2O4 @Co3O4 heterostructure nanoarrays with abundant oxygen vacancies were deposited onto nickel foam via hydrothermal reaction, cathodic electrodeposition, and CaH2 reduction. The synergistic approach boosts the electrochemical activity of catalyst by improving conductivity, mass diffusion efficiency, and active site quantity. Accordingly, the optimal electrocatalyst exhibits outstanding catalytic activity with high electrochemical stability in an alkaline medium, providing low overpotentials of 251 mV and 77 mV respectively to achieve 10 mA·cm−2 for catalyzing OER and HER. Moreover, the as-fabricated bifunctional electrocatalyst only requires a cell voltage of 1.56 V to enable a current density of 10 mA·cm−2 for realizing water electrolysis, outperforming many other previously reported catalysts. This work provides an inspiration for developing open structure materials with synergistic effect, and demonstrates a feasible method to synthesis high-efficiency electrocatalysts for water splitting.

Ten questions concerning energy flexibility in buildings

Demand side energy flexibility is increasingly being viewed as an essential enabler for the swift transition to a low-carbon energy system that displaces conventional fossil fuels with renewable energy sources while maintaining, if not improving, the operation of the energy system. Building energy flexibility may address several challenges facing energy systems and electricity consumers as society transitions to a low-carbon energy system characterized by distributed and intermittent energy resources. For example, by changing the timing and amount of building energy consumption through advanced building technologies, electricity demand and supply balance can be improved to enable greater integration of variable renewable energy. Although the benefits of utilizing energy flexibility from the built environment are generally recognized, solutions that reflect diversity in building stocks, customer behavior, and market rules and regulations need to be developed for successful implementation. In this paper, we pose and answer ten questions covering technological, social, commercial, and regulatory aspects to enable the utilization of energy flexibility of buildings in practice. In particular, we provide a critical overview of techniques and methods for quantifying and harnessing energy flexibility. We discuss the concepts of resilience and multi-carrier energy systems and their relation to energy flexibility. We argue the importance of balancing stakeholder engagement and technology deployment. Finally, we highlight the crucial roles of standardization, regulation, and policy in advancing the deployment of energy flexible buildings. • Energy flexibility characterization methodologies are needed at the aggregated level. • There is a trend toward decentralized and distributed architectures to harness energy flexibility. • Buildings with/within multicarrier energy systems offer higher levels of flexibility. • Multidisciplinary approaches are needed to address various aspects of the topic. • Supportive policies are key to enable opportunities and incentivize stakeholders.

Identification of AC Distribution Networks With Recursive Least Squares and Optimal Design of Experiment

The increasing penetration of intermittent distributed energy resources in power networks calls for novel planning and control methodologies which hinge on detailed knowledge of the grid. However, reliable information concerning the system topology and parameters may be missing or outdated for temporally varying electric distribution networks. This brief proposes an online learning procedure to estimate the network admittance matrix capturing topological information and line parameters. We start off by providing a recursive identification algorithm exploiting phasor measurements of voltages and currents. With the goal of accelerating convergence, we subsequently complement our base algorithm with a design-of-experiment procedure which maximizes the information content of data at each step by computing optimal voltage excitations. Our approach improves on existing techniques, and its effectiveness is substantiated by numerical studies on a modified IEEE testbed.

Multi-terminal phase-changing soft open point SDP modeling for imbalance mitigation in active distribution networks

Active distribution networks (ADNs) are capable of mitigating phase imbalance caused by various operational conditions, including uneven growth of single-phase and intermittent distributed energy resources (DERs), incurring financial losses or costly infrastructure reinforcements. In this paper, the research gap for a flexible phase imbalance mitigating solution is addressed by proposing a multi-terminal phase-changing soft open point (PC-SOP). It is explored in detail on balancing the power flows and compared with other different types and ways of connection (including two-terminal and conventional). Then operational strategies based on different cases are presented for imbalance mitigation. Semidefinite programming (SDP) relaxation is utilized to convert the original non-convex nonlinear model into an SDP model which can be solved efficiently by commercial solvers. Two case studies demonstrations are conducted on IEEE 13-node and 123-node three-phase networks. It is found that multi-terminal PC-SOPs can minimize power losses by between 5.56 % and 28.98% and have better voltage control (all buses operate in the allowed voltage range [0.94, 1.10]) and less PV curtailment (reduced by at least 6.31 MW/24 h and 0.63 MW/24 h for the two test networks separately) when compared to conventional SOP technologies.

A Flexible-Reliable Operation Model of Storage and Distributed Generation in a Biogas Power Plant

This paper presents a novel methodology for planning and operating biogas energy systems based on the transactive energy concept to determine multilevel operating regimes for distributed generation. The developed model is used to manage the production, storage, and dispatch of biogas energy systems to meet the load demands of the biogas producer and support the operation of the distribution network operator. An Integer Linear Programming (ILP) is fitted to optimize the biogas production of the biogas producer, including the operation of the biogas storage systems and their interaction with the network operator. The model’s objective is to maximize benefits for the participating agents in a transactive energy context. The model’s effectiveness is validated using seven case studies involving biogas systems having different operating ranges and modes to achieve enhanced flexibility and reliability for the system operation with a large proportion of intermittent energy resources. The simulation results showed that the approach could effectively manage the operation of biogas systems and their interaction with the network operator. The developed model is suitable for systems fostering net metering charging and real-time pricing.

Unbundling Smart Meter Services Through Spatiotemporal Decomposition Agents in DER-Rich Environment

Smart meters and the advanced metering infrastructure facilitate distribution system operators (DSOs) to gather information on energy consumption at the customer level. With the increasing penetration of building-level intermittent distributed energy resources (DERs) behind the meter, DER information is not available to DSOs. At the same time, the smart meter enables users to participate in grid, with real-time information. Information for behind the meter is needed by the user to coordinate building-level assets for maximum benefits. The concept of unbundled smart meter (USM) needs agents to decompose smart meter measurements to provide service to DSOs as well as customers. In this article, we propose a spatiotemporal decomposition agent (STDA) for the USM based on artificial intelligence. The STDA can help users optimize their energy usage and help DSOs to utilize building assets for the grid operation. The energy usage strategy developed by the STDA is suitable for different users and can be customized by deep learning (DL) models according to the different energy consumption habits of each user. The power prediction performance results of various DL models and evaluation using a set of data from a Hawaii utility is presented. Also, STDA integration with home energy management systems to manage resources is presented and validated. STDA preprocesses the measurements before model training and provides the spatiotemporal decomposed forecasting.

An L-1 Regularized Forecasting-Aided State Estimator for Active Distribution Networks

With the increased penetration of intermittent distributed energy resources (DERs) and the inclusion of complex loads, the states of an active distribution network (ADN) can experience sudden variations. Most distribution networks are only partially observable using available measuring devices. A distribution system state estimator (DSSE) uses pseudo, i.e., forecasted power injection measurements for the unobserved buses to perform state estimation. The pseudo measurements may fail to capture the sudden change in DERs and loads’ power. Consequently, the conventional DSSEs may fail to estimate the states when there is a sudden change in the loads and DERs connected to the unobserved buses. This paper proposes a forecasting-aided state estimator for ADNs, which can capture the sudden changes in states caused due to intermittent variations in DERs and loads’ power. The estimator requires measurements from a limited number of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> PMUs, which are placed according to the depth-of-one unobservability criterion. The present work proposes to split the state vector into two parts. The first part captures the gradual Spatio-temporal variations in the states. The second part captures temporally uncorrelated variations in the states that may occur due to the sudden change in power injections. The proposed estimator tracks both state vector components using an l1-regularization-based estimation model. The algorithm is tested on an unbalanced IEEE benchmark test feeder using real load profiles.

Emerging Best Practices for Modeling Energy Storage in Integrated Resource Plans: An overview and a comparison

Energy storage technologies are a unique class of energy resource, capable of providing service across the electric grid’s four functional levels: generation, transmission, distribution, and customer (Koohi-Fayegh and Rosen 2020). The rapid growth of intermittent energy resources, driven by state and utility decarbonization goals, is creating a growing need for energy storage to match unpredictable generation with customer demand (Gür 2018). However, the primary tool that many utilities use to identify grid investments—the integrated resource plan (IRP)—was not designed to consider the unique characteristics of energy storage.

IPlugie: Intelligent electric vehicle charging in buildings with grid-connected intermittent energy resources

Today’s energy market is increasingly integrating time-varying tariffs, peak demand charges, and/or export tariffs. In this context, intelligent charging scheduling can considerably reduce the plug-in electric vehicle (PEV) charging cost. This is especially the case as more and more PEVs are charged in buildings that are also equipped with grid-connected intermittent energy resources (IERs) (e.g., photovoltaic systems and wind turbine generators). In this work, we propose a novel and complete intelligent PEV charging scheduling system (tailored for domestic settings) that can account for peak demand charges, time-varying tariffs, and/or export tariffs, appropriately considering both potential IER generation and the rest of a building’s consumption. The backbone of our approach builds on adaptive model predictive control, and includes an efficient depth-first-search-based PEV charging planning algorithm that we propose. Importantly, our approach does not rely on a simplified linear modeling of the charging dynamics, which is a typical and limiting assumption of such systems. We evaluate our approach with real data, considering both solar and wind IER generation capacity, to show that it can reduce the cost of charging by up to ∼45% and ∼35% in the United States and the United Kingdom domestic settings, respectively, compared to standard PEV charging practices.

Ambient Temperature Sodium Polysulfide Catholyte for Nonaqueous Redox Flow Batteries

Long-duration energy storage (LDES) systems are needed to enable widespread adoption of intermittent energy resources. Redox flow batteries (RFBs) are particularly well-suited for grid-leveling applications because the system’s energy and power densities can be independently scaled. Despite exciting recent progress in both aqueous and nonaqueous systems, a major limitation of RFBs remains their high cost. For LDES applications, developing high energy density electrolytes containing earth abundant active materials is critical to meet targets for installed energy capital costs.Na-based batteries containing sulfur cathodes are attractive for low cost, high energy batteries due to sulfur’s earth abundance and high capacity (e.g., 1,672 mAh/gS for complete reduction to Na2S). Notable examples of S electrodes for reversible Na storage include: (i) molten S (e.g., high temperature Na/S batteries commercialized by NGK Insulators, Ltd. and Tokyo Electric Power Company), (ii) S-based composites containing carbon matrices designed to mitigate dissolution of sodium polysulfides (Na2Sx) in liquid electrolytes, and (iii) RFB electrolytes containing soluble Na2Sx species.This presentation will describe recent work on Na-based biphenyl|polysulfide RFBs and will highlight the effects of soluble vs. insoluble reaction products on reversible capacity and cycling stability. Full cells exhibited stable capacities (up to 200 mAh/gS) with high coulombic efficiency (99.68 ± 0.40%) and negligible capacity fade in both static and flowing cell geometries. For the first time in nonaqueous RFBs, we report the use of 3-electrode galvanostatic AC impedance measurements to identify rate limiting processes in the system. These findings demonstrate that voltage losses are dominated by charge transfer at the cathode, and relevant kinetic parameters (i.e., exchange current density and transfer coefficients) were calculated through a Tafel analysis. In this study, Na+ ß” Al2O3 (BASE) was used as a prototypical membrane, but practical systems require development of low-cost, mechanically robust membranes with high ionic conductivity and minimal crossover. Acknowledgements This research was conducted at Oak Ridge National Laboratory, managed by UT Battelle, LLC, for the U.S. Department of Energy (DOE) and is sponsored by the U.S. Department of Energy through the Energy Storage Program in the Office of Electricity.

An Intelligent Multi Agent based Approach for Autonomous Energy Management in a Microgrid

The integration of highly intermittent and unpredictable distributed energy resources increases the complexities in the energy management system (EMS). Therefore, the conventional control used in microgrid EMS is no more effective and requires alternative control strategies that can cope up with the new dynamics in the system. Adopted from computer science, a distributed artificial intelligence technique called multi agent system (MAS) can serve this purpose well due to its inherent autonomous nature, scalability, flexibility, and adaptability. Consequently, in this paper, a new multi agent based architecture for microgrid energy management system has been proposed. The proposed model considers different forecasting agents and a real time correction agent for microgrid energy management. The performance of proposed MAS based microgrid EMS modeled in a new platform called Stateflow has been evaluated by developing different scenarios considering resource fluctuations and load variations. The simulation results demonstrate the effectiveness of the proposed architecture in outperforming the conventional microgrid energy management system and the effective usage of each component of the microgrid.